Search Results (2,292)

Oxidation is a critical factor contributing to material wear and the degradation of conductive performance during current-carrying tribological processes. The present study investigated the composite oxidation mechanisms that occurred during current-carrying rolling in mixed atmospheres containing O₂ and H₂O vapor. The results obtained in a dry N₂/O₂ mixture, humid N₂, and humid N₂/O₂ mixture indicated that the oxidation mechanisms on current-carrying rolling surfaces involved thermal oxidation, tribo-oxidation, and anodic oxidation. XPS analysis confirmed that the primary oxidation product was CuO. Conductive atomic force microscopy (C-AFM) revealed that surface oxidation caused a significant reduction in conductive α-spots, leading to an increase in contact resistance. Contact resistance exhibited a quasi-linear relationship with the surface CuO content. Contact angle measurements and adhesion tests showed that the enhanced hydrophilicity of the oxidized surface and the resulting high adhesion contributed to an increase in the macroscopic friction coefficient. In humid N₂/O₂ with 50% relative humidity (RH), the friction coefficient rapidly exceeded 0.8 when the O₂ content surpassed 25%. Wear morphology analysis demonstrated that this abrupt increase in the friction coefficient induced fatigue wear on the surface. Overall, the present study elucidated the composite oxidation mechanisms during current-carrying rolling and clarified the pathways through which oxidation affected current-carrying tribological performance. These findings may contribute to improved failure analysis and the safe, reliable operation of electrical contact pairs. Full article

This study investigated the tribological and mechanical properties of chromium nitride (CrN and CrN/DLC) coatings applied to 316L steel in an artificial blood environment. The wettability of the tested surfaces was determined and the hardness was also tested using the instrumental indentation. Friction-wear tests were performed using a TRB³ tribometer in a rotating ball-on-disc configuration. The tests were performed under dry friction conditions and with lubrication using artificial blood at pH 7.45 (neutral environment) and pH 7.15 (acidified environment). Wear of the friction pairs was examined using an interferometric-confocal microscope. Artificial blood was chosen to simulate human body fluids. The use of the CrN/DLC coating reduced the coefficient of friction by 83% for dry friction, by 62% for friction with neutral artificial blood lubrication, and by 69% for friction with acidic artificial blood lubrication, respectively. Despite the increased coefficient of friction of the CrN coating, its use also contributed to reduced material wear. Full article

Pipeline steels under cyclic loading in corrosive environments are prone to wear and corrosion–wear synergy. Low-dilution, high-reliability Ni-based Cold Metal Transfer (CMT) overlays are therefore required to ensure structural integrity. In this work, three overlay architectures were deposited on L415QS pipeline steel: a single-layer ERNiFeCr-1 coating, a double-layer ERNiFeCr-1/ERNiFeCr-1 coating, and an ERNiCrMo-3 interlayer plus ERNiFeCr-1 working layer. The microstructure, interfacial composition gradients, and dry sliding wear behavior were systematically characterized to clarify the role of interlayer design. The single-layer ERNiFeCr-1 coating shows a graded transition from epitaxial columnar grains to cellular/dendritic and fine equiaxed grains, with smooth Fe dilution, Ni–Cr enrichment, and a high fraction of high-angle grain boundaries, resulting in sound metallurgical bonding and good crack resistance. The double-layer ERNiFeCr-1 coating contains coarse, strongly textured columnar grains and pronounced interdendritic segregation in the upper layer, which promotes adhesive fatigue and brittle spalling and degrades wear resistance and friction stability. The ERNiCrMo-3 interlayer introduces continuous Fe-decreasing and Ni-Cr/Mo-increasing gradients, refines grains, suppresses continuous brittle phases, and generates dispersed second phases that assist crack deflection and load redistribution. Under dry sliding, the tribological performance ranks as follows: interlayer + overlay > single-layer > double-layer. The ERNiCrMo-3 interlayer system maintains the lowest and most stable friction coefficient due to the formation of a dense tribo-oxidative glaze layer. These results demonstrate an effective hierarchical alloy-process design strategy for optimizing Ni-based CMT overlays on pipeline steels. Full article

The objective of this study is to evaluate the impact of thermal and thermochemical treatment, specifically gas nitriding, on the wear properties of AISI D2 cold work tool steel. The steel was austenitized at 1050 °C, then subjected to two annealing cycles at 560 °C for two hours each. It was then gas-nitrided for 16 and 36 h. The Vickers microhardness measurements of AISI D2 steel for the three distinct conditions, non-nitrided (NN), nitride at 16 h (N16) and nitride at 36 h (N36), are 560 HV0.1, 1050 HV0.1 and 1350 HV0.1, respectively. Wear tests were conducted utilizing a ball device, under dry friction conditions at ambient temperature, with loads of 5, 10 and 15 N, over 5000, 10,000 and 15,000 cycles at a constant sliding velocity of 30 mm/s and a sliding distance of 10 mm. Furthermore, the utilization of ANFIS modeling of experimental data facilitated the prediction of the variation in the coefficient of friction as a function of nitriding conditions and specific test parameters. The results show a significant effect of nitriding, leading to a marked reduction in the coefficient of friction. In the non-nitrided condition, the average value reaches 0.80, while extended nitriding to 36 h reduces this value to around 0.49, confirming a substantial tribological improvement. This enhancement is ascribed to the formation of hard, resilient nitride layers on the steel surface, thereby increasing wear resistance and cur-tailing in industrial applications. Full article

Soil permeability is an important factor in the mining and geotechnical industry, impacting slope stability and tailings management. It directly influences the stability of structures, the control of water in tailings ponds, and the safety of workers. Various additives, such as cement kiln dust (CKD), bentonite, fly ash, polymers, lime, and asphalt, are incorporated into soil structures to improve permeability and stability. Any significant changes in soil permeability will alter the soil’s behavior. However, the long-term effect of most additives on structures remains unexplored. This study investigates the long-term impact of CKD on the permeability of a CKD-treated slope. The slope surface was treated with 0%, 5%, 10%, and 15% of CKD by the dry weight of the soil in 2008 and was evaluated in 2024. The permeability test results of the collected soil sample from the slope (2024) showed that the permeability of the soil decreases with an increase in the soil CKD content. The coefficient of permeability, k, is more than 100 times less for a CKD content of 15% by the dry weight of the soil compared to the permeability of the untreated native soil. The treated soil becomes almost impermeable when the CKD content increases to 20% (by the dry weight of the soil). However, the treated slope’s permeability increased over time, possibly due to erosion, resulting in a reduction in CKD content. The surface permeability of the slope exhibits an irregular distribution, resulting from the evolving spatial distribution of Cement Kiln Dust over time. Full article

Soil salinization is an important negative factor that reduces the fertility of irrigated arable land. The fields in southern Kazakhstan are at high risk of salinization due to the dry arid climate. In some cases, even the top layer of soil has a significant degree of salinization. The use of a UAV equipped with a multispectral camera can help in the rapid and highly detailed mapping of salinity in cultivated arable land. This article describes the process of preparing the labeled data for assessing the salinity of the top layer of soil and the comparative results achieved due to using machine learning methods in two different districts. During an expedition to the fields of the Turkestan region of Kazakhstan, fields were surveyed using a multispectral camera mounted on a UAV; simultaneously, the soil samples were collected. The electrical conductivity of the soil samples was then measured in laboratory conditions, and a set of programs was developed to configure machine learning models and to map the obtained results subsequently. A comparative analysis of the results shows that local conditions have a significant impact on the quality of the models in different areas of the region, resulting in differences in the composition and significance of the model input parameters. For the fields of the Zhetisay district, the best result was achieved using the extreme gradient boosting regressor model (linear correlation coefficient Rp = 0.86, coefficient of determination R² = 0.42, mean absolute error MAE = 0.49, mean square error MSE = 0.63). For the fields in the Shardara district, the best results were achieved using the support vector machines model (Rp = 0.82, R² = 0.22, MAE = 0.41, MSE = 0.46). This article presents the results, discusses the limitations of the developed technology for operational salinity mapping, and outlines the tasks for future research. Full article

The objective of this work was to evaluate the potential of PEF application on the decrease in orange peel air-drying time and temperature, resulting in energy savings. Orange peel waste (by-product of squeezable orange juice typical production, with a moisture content of 70%) was PEF pretreated (1.0–5.0 kV/cm electric field strength, frequency of 20 Hz, pulse width 15 μs, >1000 pulses), achieving a cell disintegration index Z ranging from 0.1 to 0.8. Drying experiments of PEF-treated orange peels were carried out at mild temperatures (40–70 °C). The moisture diffusion coefficients D_eff and the air-drying energy consumed of all samples were estimated and compared. At low drying temperatures (<55 °C), PEF treatment led to increased effective moisture diffusivity D_eff by up to 25%, resulting in reduced drying time and energy savings up to 15 MJ/kg, compared to untreated samples. More intense PEF conditions resulted in higher drying rates, while, for temperatures > 60 °C, there was no significant effect on the moisture diffusion coefficient for PEF pretreated samples. PEF treatment did not lead to changes in the antioxidant activity of dried samples. The results showed the potential of PEF pretreatment to accelerate the drying process of orange peel waste minimizing energy consumption. Full article

The global shift to digital terrestrial television broadcasting (DTTB) from the conventional analogue has significantly transformed television culture, necessitating comprehensive technical and infrastructural evaluations. This study addresses the limitations of existing path-loss models for accurately predicting path loss in digital terrestrial television broadcasting in the UHF bands, motivated by the need for reliable, location-specific models that account for seasonal, meteorological, and topographical variations in Abeokuta, Nigeria. The study focuses on path-loss prediction in the UHF band using Ogun State Television (OGTV), Abeokuta, Nigeria, as the transmission source. Eight receiving sites, spaced 2 kilometers apart, were selected along a 16.7 km transmission contour. Daily measurements of received signal strength (RSS) and weather conditions were collected over one year. Seasonal path-loss models ${PL}_{wet}$ for the wet season and ${PL}_{dry}$. For the dry season, models were developed using multiple regression analysis and further optimized using least squares (LS) and gradient descent (GD) techniques, resulting in six refined models: ${PL}_{wet}$, ${PL}_{dry}$, ${PL}_{wet-LS}$, ${PL}_{dry-LS}$, ${PL}_{wet-GD}$, and ${PL}_{dry-GD}$. Model performance was evaluated using Mean Absolute Error, Root Mean Square Error, Coefficient of Correlation, and Coefficient of Multiple Determination. Results indicate that the Okumura model provided the closest approximation to measured RSS for all the receiving sites, while the Hata and COST-231 models were unsuitable. Among the developed models, ${PL}_{wet}$ ($RMSE- 1.2633$, $MAE - 0.9968$, $MSE - 1.5959$, $R - 0.9935$, $R^2 - 0.9871$) and ${PL}_{dry-LS}$($RMSE- 1.1884$, $MAE - 0.7692$, $MSE - 1.4124$, $R - 0.9942$, $R^2 - 0.9883$) were found to be the most suitable models for the wet and dry seasons, respectively. The major influence of location-based elevation and meteorological data on path-loss prediction over digital terrestrial television broadcasting communication lines in Ultra-High-Frequency bands was evident. Full article

The wandering reach of the Yellow River has long been a pivotal area of research due to its drastic fluctuations in water-sediment dynamics, frequent shifts in the main channel, and complex river regime evolution. Studies on the main-channel morphological evolution in this reach have focused on the analysis of parameters related to the overall oscillation or have only analyzed a certain reach within the wandering reach, with a lack of detailed studies based on the different characteristics of each area. Therefore, taking the Xiaolangdi Reservoir–Gaocun reach as the research area, by constructing a two-dimensional water-sediment dynamic model, the erosion–deposition characteristics of different sub-reaches and the morphological evolution characteristics of key cross-sections were quantified and analyzed. Based on measured hydrological, sediment, and topographic data, the temporal and spatial changes in the bankfull area and fluvial facies coefficient of typical sections before and after the construction of Xiaolangdi Reservoir were analyzed. By interpreting remote sensing images, the spatio-temporal variation characteristics of the migration distance and bending coefficient of different reaches before and after the construction of Xiaolangdi Reservoir were calculated, and the key factors influencing the evolution of river morphology parameters were identified. The results showed that after the Xiaolangdi Reservoir operation, the overall erosion of the Huayuankou–Jiahetan reach is greater than the deposition, and the erosion is more obvious in dry years. The river course direction and control engineering play a significant role in controlling the morphological evolution of the main channel during the process, causing the R2 reach to significantly swing to the north bank and the R3 reach to the south bank. When the sediment transport coefficient values were between 0 and 0.005 kg.s.m⁻⁶, water-sediment had a positive effect on shaping and evolving the main-channel morphology. The long-term low-sand discharge of Xiaolangdi Reservoir and the continuous improvement of river regulation projects are the main reasons for the above changes. The results can provide support for controlling the evolution of the main channel and improving river regulation projects. Full article

Accurate daily runoff forecasting is essential for flood control and water resource management, yet existing models struggle with the seasonal non-stationarity and inter-basin variability of runoff sequences. This paper proposes a Season-Aware Ensemble Forecasting (SAEF) method that integrates SVM, LSSVM, LSTM, and BiLSTM models to leverage their complementary strengths in capturing nonlinear and non-stationary hydrological dynamics. SAEF employs a seasonal segmentation mechanism to divide annual runoff data into four seasons (spring, summer, autumn, winter), enhancing model responsiveness to seasonal hydrological drivers. An Improved Arctic Puffin Optimization (IAPO) algorithm optimizes the model weights, improving prediction accuracy. Beyond numerical gains, the framework also reflects seasonal runoff generation processes—such as rapid rainfall–runoff in wet seasons and baseflow contributions in dry periods—providing a physically interpretable perspective on runoff dynamics. The effectiveness of SAEF was validated through case studies in the Dongjiang Hydrological Station (China), the Elbe River (Germany), and the Quinebaug River basin (USA), using four performance metrics (MAE, RMSE, NSEC, KGE). Results indicate that SAEF achieves average Nash–Sutcliffe Efficiency Coefficient (NSEC) and Kling–Gupta efficiency (KGE) coefficients of over 0.92, and 0.90, respectively, significantly outperforming individual models (SVM, LSSVM, LSTM, BiLSTM) with RMSE reductions of up to 58.54%, 55.62%, 51.99%, and 48.14%. Overall, SAEF not only strengthens predictive accuracy across diverse climates but also advances hydrological understanding by linking data-driven ensembles with seasonal process mechanisms, thereby contributing a robust and interpretable tool for runoff forecasting. Full article

To address the limitations of existing fault diagnosis methods for excitation dry-type transformers, such as inadequate utilization of multi-axis vibration data, low recognition accuracy under complex operational conditions, and limited computational efficiency, this paper presents a lightweight fault diagnosis approach based on the fusion of multi-channel vibration signals and visual features. Initially, a multi-physics field coupling simulation model of the excitation dry-type transformer is developed. Vibration data collected from field-installed three-axis sensors are combined to generate typical fault samples, including normal operation, winding looseness, core looseness, and winding eccentricity. Due to the high dimensionality of vibration signals, the Symmetrized Dot Pattern (ISDP) method is extended to aggregate and map time- and frequency-domain information from the x-, y-, and z-axes into a two-dimensional feature map. To optimize the inter-class separability and intra-class consistency of the map, Particle Swarm Optimization (PSO) is employed to adaptively adjust the angle gain factor (η) and time delay coefficient (t). Keypoint descriptors are then extracted from the map using the Oriented FAST and Rotated BRIEF (ORB) feature extraction operator, which improves computational efficiency while maintaining sensitivity to local details. Finally, an efficient fault classification model is constructed using an Adaptive Boosting Support Vector Machine (Adaboost-SVM) to achieve robust fault mode recognition across multiple operating conditions. Experimental results demonstrate that the proposed method achieves a fault diagnosis accuracy of 94.00%, outperforming signal-to-image techniques such as Gramian Angular Field (GAF), Recurrence Plot (RP), and Markov Transition Field (MTF), as well as deep learning models based on Convolutional Neural Networks (CNN) in both training and testing time. Additionally, the method exhibits superior stability and robustness in repeated trials. This approach is well-suited for online monitoring and rapid diagnosis in resource-constrained environments, offering significant engineering value in enhancing the operational safety and reliability of excitation dry-type transformers. Full article

The limited heat stability of skim milk powder (SMP) constrains its application in high-temperature processes. While dry heating can improve its thermal resistance, it often accelerates the advanced Maillard reaction, compromising protein quality. This study applied low relative humidity conditions (<10% RH) during dry heating to modulate the Maillard reaction, aiming to enhance the heat resistance of SMP and derive recombined filled evaporated milk emulsions with fewer undesirable changes in colour and solubility. SMP was subjected to dry heating at 80, 100, and 120 °C for durations ranging from 2 to 20 min (at 120 °C) and up to 16 h (at 80 °C). The progression of the Maillard reaction and associated protein modifications were evaluated. The results indicate that the advanced Maillard reaction was retarded, evidenced by minimal colour development and well-preserved protein solubility (90–97%, n = 3), determined using the Lowry assay on the supernatants. The hydroxymethylfurfural and protein carbonyl contents increased only moderately with temperature and time. Moreover, the sulfhydryl group content remained largely stable, consistent with limited disulfide-mediated aggregation. Heat treatment of SMP at 120 °C for 10 min greatly improved its heat stability, as reflected by a 25-fold reduction in the volume-weighted average diameter (D_4,3; 95% CI = 3 to 47) and a 108-fold reduction in the consistency coefficient (K; 95% CI = 12 to 200) of the SMP-derived sterilised recombined filled evaporated milk (RFEM) compared to the control. These findings demonstrate that dry heating under low RH helps to improve the functional properties of SMP without inducing the detrimental effects associated with advanced Maillard products. Full article

This study presents a detailed analysis of the morphological evolution of beaches in the Bocagrande sector of Cartagena de Indias, Colombia, over more than a decade, based on periodic monitoring of six beach profiles. The beaches in this area are in bays constrained by headlands and promontories located at both ends of each bay. Changes in shoreline position, dry beach widths, and the surf zone were evaluated using aerial photographs, orthophotos, satellite imagery, and field data, together with sediment size determined through granulometric analysis. The results indicate that the beaches exhibit characteristics of wave-dominated, exposed systems, with sediments classified as fine sand that tend to increase in grain size toward the northern sector of the bay. A cyclical variation in the shoreline was observed, with average retreats and advances ranging from 5 to 10 m, depending on the climatic season. Dry beach widths ranged from 10 to 90 m, decreasing toward the north. Differences in morphology between profiles and shoreline variation are attributed to the climatic season, profile location within the bay, and proximity to a coastal structure and its particular type. Beach profiles were fitted to conceptual equilibrium profile models using traditional equations, which yielded a coefficient of determination of 0.76; when machine learning algorithms were applied, this value improved to 0.99. This study provides an important baseline for future morphological assessments and coastal management efforts in the city and places with similar characteristics, particularly considering ongoing shoreline protection projects. Full article

Ground-penetrating radar (GPR) is widely used for thickness or compaction degree detection of asphalt pavement layers, where the dielectric properties of asphalt mixtures serve as a key parameter influencing detection accuracy. These properties are closely related to the composition of the mixture and are susceptible to environmental factors such as water or ice. To clarify the influence of various factors on the dielectric behavior of asphalt mixtures, an experimental study was conducted under controlled environmental conditions. Asphalt mixture specimens with different air void contents (5.49~10.29%) were prepared, and variables such as void fraction, moisture, and ice presence were systematically controlled. Air-coupled GPR was employed to measure the specimens, and the relative permittivity was calculated using both the reflection coefficient method (RCM) and the thickness inversion algorithm (TIA). Discrepancies between the two methods were compared and analyzed. Results indicate that the RCM is significantly influenced by surface water or ice and is only suitable for dielectric characterization under dry pavement conditions. In contrast, the TIA yields more reliable results across varying surface environments. A unified model (the optimized shape factor u = −4.5 and interaction coefficient v = 5.1) was established to describe the relationship between the dielectric properties of asphalt mixtures and their volumetric parameters (bulk specific density, air void content, voids in mineral aggregate, and voids filled with asphalt). This study enables quantitative analysis of the effects of water, ice, and mixture composition on the dielectric properties of asphalt mixtures, providing a scientific basis for non-destructive and accurate GPR-based evaluation of asphalt pavements. Full article

Accurate, high-throughput estimation of Above-Ground Biomass (AGB), a key predictor of yield, is a critical goal in rapeseed breeding. However, this is constrained by two key challenges: (1) traditional measurement is destructive and laborious, and (2) modern deep learning approaches require vast, costly labeled datasets. To address these issues, we present a data-efficient deep learning framework using smartphone-captured top-down RGB images for AGB estimation (Fresh Weight, FW, and Dry Weight, DW). Our approach utilizes a two-stage strategy where a Vision Transformer (ViT) backbone is first pre-trained on a large, aggregated dataset of diverse, non-rapeseed public plant datasets using the DINOv2 self-supervised learning (SSL) method. Subsequently, this pre-trained model is fine-tuned on a small, custom-labeled rapeseed dataset (N = 833) using a Multi-Task Learning (MTL) framework to simultaneously regress both FW and DW. This MTL approach acts as a powerful regularizer, forcing the model to learn robust features related to the 3D plant structure and density. Through rigorous 5-fold cross-validation, our proposed model achieved strong predictive performance for both Fresh Weight (Coefficient of Determination, R² = 0.842) and Dry Weight (R² = 0.829). The model significantly outperformed a range of baselines, including models trained from scratch and those pre-trained on the generic ImageNet dataset. Ablation studies confirmed the critical and synergistic contributions of both domain-specific SSL (vs. ImageNet) and the MTL framework (vs. single-task training). This study demonstrates that an SSL+MTL framework can effectively learn to infer complex 3D plant attributes from 2D images, providing a robust and scalable tool for non-destructive phenotyping to accelerate the rapeseed breeding cycle. Full article